9,588 research outputs found
How Scale Affects Structure in Java Programs
Many internal software metrics and external quality attributes of Java
programs correlate strongly with program size. This knowledge has been used
pervasively in quantitative studies of software through practices such as
normalization on size metrics. This paper reports size-related super- and
sublinear effects that have not been known before. Findings obtained on a very
large collection of Java programs -- 30,911 projects hosted at Google Code as
of Summer 2011 -- unveils how certain characteristics of programs vary
disproportionately with program size, sometimes even non-monotonically. Many of
the specific parameters of nonlinear relations are reported. This result gives
further insights for the differences of "programming in the small" vs.
"programming in the large." The reported findings carry important consequences
for OO software metrics, and software research in general: metrics that have
been known to correlate with size can now be properly normalized so that all
the information that is left in them is size-independent.Comment: ACM Conference on Object-Oriented Programming, Systems, Languages and
Applications (OOPSLA), October 2015. (Preprint
Aspect-Oriented Programming
Aspect-oriented programming is a promising idea that can improve the quality of software by reduce the problem of code tangling and improving the separation of concerns. At ECOOP'97, the first AOP workshop brought together a number of researchers interested in aspect-orientation. At ECOOP'98, during the second AOP workshop the participants reported on progress in some research topics and raised more issues that were further discussed. \ud
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This year, the ideas and concepts of AOP have been spread and adopted more widely, and, accordingly, the workshop received many submissions covering areas from design and application of aspects to design and implementation of aspect languages
From Query to Usable Code: An Analysis of Stack Overflow Code Snippets
Enriched by natural language texts, Stack Overflow code snippets are an
invaluable code-centric knowledge base of small units of source code. Besides
being useful for software developers, these annotated snippets can potentially
serve as the basis for automated tools that provide working code solutions to
specific natural language queries.
With the goal of developing automated tools with the Stack Overflow snippets
and surrounding text, this paper investigates the following questions: (1) How
usable are the Stack Overflow code snippets? and (2) When using text search
engines for matching on the natural language questions and answers around the
snippets, what percentage of the top results contain usable code snippets?
A total of 3M code snippets are analyzed across four languages: C\#, Java,
JavaScript, and Python. Python and JavaScript proved to be the languages for
which the most code snippets are usable. Conversely, Java and C\# proved to be
the languages with the lowest usability rate. Further qualitative analysis on
usable Python snippets shows the characteristics of the answers that solve the
original question. Finally, we use Google search to investigate the alignment
of usability and the natural language annotations around code snippets, and
explore how to make snippets in Stack Overflow an adequate base for future
automatic program generation.Comment: 13th IEEE/ACM International Conference on Mining Software
Repositories, 11 page
Stack Overflow in Github: Any Snippets There?
When programmers look for how to achieve certain programming tasks, Stack
Overflow is a popular destination in search engine results. Over the years,
Stack Overflow has accumulated an impressive knowledge base of snippets of code
that are amply documented. We are interested in studying how programmers use
these snippets of code in their projects. Can we find Stack Overflow snippets
in real projects? When snippets are used, is this copy literal or does it
suffer adaptations? And are these adaptations specializations required by the
idiosyncrasies of the target artifact, or are they motivated by specific
requirements of the programmer? The large-scale study presented on this paper
analyzes 909k non-fork Python projects hosted on Github, which contain 290M
function definitions, and 1.9M Python snippets captured in Stack Overflow.
Results are presented as quantitative analysis of block-level code cloning
intra and inter Stack Overflow and GitHub, and as an analysis of programming
behaviors through the qualitative analysis of our findings.Comment: 14th International Conference on Mining Software Repositories, 11
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